Soccer analysis example¶

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This tutorial uses data extracted from video footage of a soccer game that was published in https://github.com/Friends-of-Tracking-Data-FoTD/Last-Row

In [1]:
import numpy as np
import pandas as pd
from geopandas import GeoDataFrame, read_file
from shapely.geometry import Point, LineString, Polygon
from datetime import datetime, timedelta
import matplotlib.pyplot as plt
import movingpandas as mpd
from holoviews import opts, dim
import holoviews as hv

import warnings
warnings.simplefilter("ignore")

INFO: Missing optional dependencies. To use the trajectory smoother classes please install Stone Soup (see https://stonesoup.readthedocs.io/en/latest/#installation).
In [2]:
hvplot_defaults = {'line_width':5, 'frame_height':350, 'frame_width':700, 'colorbar':True, 'tiles':None, 'geo':False,}
In [3]:
mpd.__version__
Out[3]:
'0.9.rc3'

Loading soccer dataset from Github¶

In [4]:
from os.path import exists
from urllib.request import urlretrieve

def get_file_from_url(url):
    file = url.split('/')[-1]
    if not exists(file):
        urlretrieve(url, file)
    return file     

def get_df_from_gh_url(url):
    file = get_file_from_url(url)
    return pd.read_csv(file)
In [5]:
input_file = "https://raw.githubusercontent.com/Friends-of-Tracking-Data-FoTD/Last-Row/master/datasets/positional_data/liverpool_2019.csv"
df = get_df_from_gh_url(input_file)
df.drop(columns=['Unnamed: 0'], inplace=True)
print(f'Number of records: {len(df)}')

Number of records: 74936
In [6]:
df.head()
Out[6]:
bgcolor dx dy edgecolor frame play player player_num team x y z
0 NaN 0.000000 0.000000 NaN 0 Liverpool [3] - 0 Bournemouth 0 NaN NaN 46.394558 11.134454 0.0
1 NaN 0.185745 1.217580 NaN 1 Liverpool [3] - 0 Bournemouth 0 NaN NaN 46.580302 12.352034 0.0
2 NaN 0.178659 1.171133 NaN 2 Liverpool [3] - 0 Bournemouth 0 NaN NaN 46.758961 13.523166 0.0
3 NaN 0.171573 1.124685 NaN 3 Liverpool [3] - 0 Bournemouth 0 NaN NaN 46.930535 14.647852 0.0
4 NaN 0.164488 1.078238 NaN 4 Liverpool [3] - 0 Bournemouth 0 NaN NaN 47.095022 15.726090 0.0

From the metadata:

  • play: the scoreline after the goal. The team who scored the goal is the one next to the brackets.
  • frame: the frame number for the current location. Data provided has 20 frames per second.
  • player: the id of the player. The id is consistent within a play but not between plays.
  • player_num: the player jersey number. This number is the official one, and did not change for Liverpool in 2019. You can check the corresponding names at this wikipedia link.
  • x, y: coordinates for the player/ball. Pitch coordinates go from 0 to 100 on each axis.
  • dx, dx: change in (x,y) coordinates from last frame to current frame
  • z: height, from 0 to 1.5 (only filled for the ball)
  • bgcolor: the main color for the team (used as background color)
  • edgecolor the secondary color (used as edge color)

And accoring to https://en.wikipedia.org/wiki/Football_pitch

the preferred size for many professional teams' stadiums is 105 by 68 metres

In [7]:
plays = list(df.play.unique())

def to_timestamp(row):
    # plays to date
    day = plays.index(row.play)+1
    start_time = datetime(2019,1,day,12,0,0)
    # frames to time
    td = timedelta(milliseconds=1000/20*row.frame)
    return start_time + td

# frame: the frame number for the current location. Data provided has 20 frames per second
df['time'] = df.apply(to_timestamp, axis=1)
df.set_index('time', inplace=True)

# the preferred size for many professional teams' stadiums is 105 by 68 metres, accoring to https://en.wikipedia.org/wiki/Football_pitch
pitch_length = 105
pitch_width = 68
df.x = df.x / 100 * pitch_length 
df.y = df.y / 100 * pitch_width

df
Out[7]:
bgcolor dx dy edgecolor frame play player player_num team x y z
time
2019-01-01 12:00:00.000 NaN 0.000000 0.000000 NaN 0 Liverpool [3] - 0 Bournemouth 0 NaN NaN 48.714286 7.571429 0.0
2019-01-01 12:00:00.050 NaN 0.185745 1.217580 NaN 1 Liverpool [3] - 0 Bournemouth 0 NaN NaN 48.909318 8.399383 0.0
2019-01-01 12:00:00.100 NaN 0.178659 1.171133 NaN 2 Liverpool [3] - 0 Bournemouth 0 NaN NaN 49.096909 9.195753 0.0
2019-01-01 12:00:00.150 NaN 0.171573 1.124685 NaN 3 Liverpool [3] - 0 Bournemouth 0 NaN NaN 49.277061 9.960539 0.0
2019-01-01 12:00:00.200 NaN 0.164488 1.078238 NaN 4 Liverpool [3] - 0 Bournemouth 0 NaN NaN 49.449774 10.693741 0.0
... ... ... ... ... ... ... ... ... ... ... ... ...
2019-01-19 12:00:06.000 blue 0.000000 0.000000 white 120 Leicester 0 - [3] Liverpool 10267 NaN defense 103.661067 36.529840 0.0
2019-01-19 12:00:06.050 blue 0.000000 0.000000 white 121 Leicester 0 - [3] Liverpool 10267 NaN defense 103.661067 36.529840 0.0
2019-01-19 12:00:06.100 blue 0.000000 0.000000 white 122 Leicester 0 - [3] Liverpool 10267 NaN defense 103.661067 36.529840 0.0
2019-01-19 12:00:06.150 blue 0.000000 0.000000 white 123 Leicester 0 - [3] Liverpool 10267 NaN defense 103.661067 36.529840 0.0
2019-01-19 12:00:06.200 blue 0.000000 0.000000 white 124 Leicester 0 - [3] Liverpool 10267 NaN defense 103.661067 36.529840 0.0

74936 rows × 12 columns

In [8]:
df['team'].value_counts().plot(title='team', kind='bar', figsize=(15,3))
Out[8]:
<AxesSubplot:title={'center':'team'}>
In [9]:
df['player_num'].value_counts().plot(title='player_num', kind='bar', figsize=(15,3))
Out[9]:
<AxesSubplot:title={'center':'player_num'}>
In [10]:
df['team'] = df['team'].astype('category').cat.as_ordered()
df['player'] = df['player'].astype('category').cat.as_ordered()
df['player_num'] = df['player_num'].astype('category').cat.as_ordered()

Finally, let's create trajectories:

Trajectories¶

In [11]:
%%time
CRS = None
traj_collection = mpd.TrajectoryCollection(df, 'player', x='x', y='y', crs=CRS)
mpd.TemporalSplitter(traj_collection).split(mode="day")
print(f"Finished creating {len(traj_collection)} trajectories")

Finished creating 364 trajectories
CPU times: total: 33.6 s
Wall time: 35.3 s
In [12]:
pitch = Polygon([(0, 0), (0, pitch_width), (pitch_length, pitch_width), (pitch_length, 0), (0, 0)])
plotted_pitch = GeoDataFrame(pd.DataFrame([{'geometry': pitch, 'id': 1}]), crs=CRS).hvplot(color='white', alpha=0.5)
In [13]:
plotted_pitch * traj_collection.filter('player_num', 20).hvplot(**hvplot_defaults)
Out[13]:

Plays¶

In [14]:
PLAY = 2
title = f'Play {PLAY} {plays[PLAY]}'
play_trajs = traj_collection.filter('play', plays[PLAY])
play_trajs
Out[14]:
TrajectoryCollection with 20 trajectories
In [15]:
play_trajs.plot(column='team', colormap={'attack':'hotpink', 'defense':'turquoise'})
Out[15]:
<AxesSubplot:>
In [16]:
generalized = mpd.MinTimeDeltaGeneralizer(play_trajs).generalize(tolerance=timedelta(seconds=0.5))
In [17]:
generalized.hvplot(title=title,  c='speed', hover_cols=['player', 'team'], **hvplot_defaults)
Out[17]:
In [18]:
(
    plotted_pitch * 
    generalized.hvplot(title=title,  c='speed', hover_cols=['player'], cmap='Viridis', **hvplot_defaults)
)
Out[18]:
In [19]:
get_file_from_url('https://github.com/anitagraser/movingpandas/raw/master/tutorials/data/soccer_field.png')

pitch_img = hv.RGB.load_image('soccer_field.png', bounds=(0,0,pitch_length,pitch_width)) 
(
    pitch_img * 
    generalized.hvplot(title=title, c='team', colormap={'attack':'limegreen', 'defense':'purple'}, 
                    hover_cols=['player'],**hvplot_defaults) * 
    generalized.get_start_locations().hvplot(label='start', color='orange')
)
Out[19]:

Continue exploring MovingPandas¶

  1. Bird migration analysis
  2. Ship data analysis
  3. Horse collar data exploration
  4. OSM traces
  5. Soccer game
  6. Mars rover & heli
In [ ]: